We recently published two real-world scenarios demonstrating how to use Azure Machine Learning alongside the Team Data Science Process (TDSP) to execute AI projects involving Natural Language Processing (NLP) use-cases, namely, for sentiment classification and entity extraction. This blog post provides a summary of these two samples, which are available through public GitHub repositories. The samples use a variety of Azure data platforms, such as Data Science Virtual Machines (DSVMs) to train DNN models for sentiment classification and entity extraction using GPUs, and HDInsight Spark for data processing and word embedding model training at scale. The samples show how domain-specific word embeddings generated using domain-specific and labeled training data sets outperforms generic word embeddings trained on general and unlabeled data, which leads to improved accuracy in classification and entity extraction tasks.

Microsoft is at the forefront of speech recognition, having reached human parity on the Switchboard research benchmark. This technology is truly capable of transforming our daily lives, as it indeed already has started to, be it through digital assistants, or our ability to dictate emails and documents, or via transcriptions of lectures and meetings. These scenarios are possible thanks to years of research and recent technological jumps enabled by neural networks. As part of our mission to empower developers with our latest AI advances, we now offer a spectrum of Cognitive Services APIs, addressing a range of developer scenarios. For scenarios that require the use of domain specific vocabularies or the need to navigate complex acoustic conditions, we offer the Custom Speech Service which lets developers automatically tune speech recognition models to their needs.

As an example, a university may be interested to accurately transcribe and digitize all their lectures. A given lecture in biology, to cite one example, may include a term such as “Nerodia erythrogaster”. Although extremely domain-specific, terms

06

Feb

This post is authored by Chris Testa-O’Neill, Applied Data Scientist in the Microsoft Cloud AI team.

Artificial Intelligence (AI) is proving to be a massively disruptive force, one that is leading to the digital transformation of businesses at a faster pace than most of us would have imagined. At Microsoft, our mission is to bring AI to every developer and every organization on the planet, and to provide the best platform and tools to make them successful. You can read more Microsoft’s approach to AI here.

In keeping with our mission, we are currently running a series of popular AI boot camps around the world. This post shares more information about these boot camps and provides links for you to access these materials and start building your own AI apps in a self-paced way.

Our bootcamps have two target audience profiles, the emerging AI developer and the professional AI developer, and our curriculum is primarily oriented towards these two personas. The first two days of the bootcamp are aimed at the emerging AI developer. The target profile here is an IT developer who is yet to use Microsoft AI tools and APIs to infuse intelligence into their business applications.

There are an increasing number of useful applications of machine learning and Artificial Intelligence in the domain of audio, such as in home surveillance (e.g. detecting glass breaking and alarm events); security (e.g. detecting sounds of explosions and gun shots); driverless cars (sound event detection for increased safety); predictive maintenance (forecast machine failures in a manufacturing process based on vibrations); for real-time translation in Skype and even for music synthesis.

The human brain processes such a wide variety of sounds so effortlessly – be it the bark of puppies, audible alarms from smoke or carbon monoxide detectors, or people talking loudly in a coffee shop – that most of us tend to take this faculty for granted. But what if we could apply AI to help the hearing impaired achieve something similar? That would be something special.

Microsoft has recently launched Azure Machine Learning services (AML) to public preview. The updated services include a Workbench application plus command-line tools to assist in developing and managing machine learning solutions through the entire data science life cycle. An Experimentation Service handles the execution of ML experiments and provides project management, Git integration, access control, roaming, and sharing of work. The Model Management Service allows data scientists and dev-ops teams to deploy predictive models into a wide variety of environments. Model versions and lineage are tracked from training runs to deployments while being stored, registered, and managed in the cloud.

Once AML Workbench is installed, the app connects to a Gallery of prebuilt real world data science scenario projects to help new users explore Azure ML, as well as give users a jump start on their specific data science scenarios.

The AML gallery currently contains two predictive maintenance example scenarios:

Artificial Intelligence (AI) is a hugely disruptive force, one that is powering much of the digital transformation businesses are going through in recent times. At Microsoft, our mission is to bring AI to every developer and every organization on the planet, and to help businesses augment human ingenuity in unique and differentiated ways.

Developers and data scientists are at the heart of this transformation and the mission for the Microsoft AI platform is to offer the very best tools to make them successful in this journey. These include tools for automating machine learning through the pre-built AI capabilities we offer for vision, speech, language, knowledge and search in the form of the Microsoft Cognitive Services, which are enabling a rich variety of customer scenarios. As an example, when we announced the general availability of our conversational AI tools last month, we showcased innovative applications from leading edge customers such as Molson Coors, UPS and many others.

We continue to innovate on our AI platform at a rapid pace and wish to make AI easy by bringing capabilities such as transfer learning and automated machine learning to developers.

18

Jan

This is the first in a multi-part series by guest blogger Adrian Rosebrock. Adrian writes at PyImageSearch.com about computer vision and deep learning using Python, and he recently finished authoring a new book on deep learning for computer vision and image recognition.

Introduction

I had two goals when I set out to write my new book, Deep Learning for Computer Vision with Python. The first was to create a book/self-study program that was accessible to both novices and experienced researchers and practitioners — we start off with the fundamentals of neural networks and machine learning and by the end of the program you’re training state-of-the-art networks on the ImageNet dataset from scratch. My second goal was to provide a book that included:

Practical walkthroughs that present solutions to actual, real-world deep learning classification problems. Hands-on tutorials (with accompanying code) that not only show you the algorithms behind deep learning for computer vision but their implementations as well. A no-nonsense teaching style that cuts through all the cruft and helps you on your path to deep learning + computer vision mastery for visual recognition.

Along the way I quickly realized that a stumbling block for many readers is configuring their development environment — especially true for

18

Jan

This is the first in a multi-part series by guest blogger Adrian Rosebrock. Adrian writes at PyImageSearch.com about computer vision and deep learning using Python, and he recently finished authoring a new book on deep learning for computer vision and image recognition.

Introduction

I had two goals when I set out to write my new book, Deep Learning for Computer Vision with Python. The first was to create a book/self-study program that was accessible to both novices and experienced researchers and practitioners — we start off with the fundamentals of neural networks and machine learning and by the end of the program you’re training state-of-the-art networks on the ImageNet dataset from scratch. My second goal was to provide a book that included:

Practical walkthroughs that present solutions to actual, real-world deep learning classification problems. Hands-on tutorials (with accompanying code) that not only show you the algorithms behind deep learning for computer vision but their implementations as well. A no-nonsense teaching style that cuts through all the cruft and helps you on your path to deep learning + computer vision mastery for visual recognition.

Along the way I quickly realized that a stumbling block for many readers is configuring their development environment — especially true for

10

Jan

This post is authored by Gopi Kumar, Principal Program Manager, and Paul Shealy, Senior Software Engineer at Microsoft.

With the rise of Artificial Intelligence, the need to rapidly train a large number of data scientists and AI developers has never been more urgent. Microsoft is always looking for efficient ways to educate employees and customers on AI and make them more productive when using these new capabilities. Aside from numerous technical conferences that we host and sponsor, we also offer the AI School and a range of tools such as the Data Science Virtual Machine, Visual Studio Tools for AI, Azure Machine Learning, Microsoft ML Server, and Batch AI to help developers and data scientists become more productive around building their intelligent AI-infused apps.

Pulling together deep learning workshops for a large number of students, however, can be a time consuming, error prone, and costly exercise. Furthermore, technical issues with the environment setup and compatibility problems during the workshops impede learning and cause student dissatisfaction. These workshops typically have participants bring their laptops and have them download and install new software. However, with the wide range of laptop platforms (Windows, Mac, Linux), numerous configurations, and version conflicts with existing software, workshops

03

Jan

As we ring in the new year, we’d like to kick things off in our usual fashion – with a quick recap of our most popular posts from the year just concluded. So here are our “Top 10” posts from 2017, sorted in increasing order of readership – enjoy!

Lung cancer – which is the leading cancer when it comes to mortality in both women and men in the US – suffers from a low rate of early diagnosis. The Data Science Bowl competition aimed to help by having participants use machine learning to determine whether CT scans of the lung have cancerous lesions or not. Success in the competition required that data scientists get started quickly and iterate rapidly. Through this post, we showed how to compute features of scanned images with a pre-trained Convolutional Neural Network (CNN), and use these features to classify scans as cancerous or not using a boosted tree – all within one hour.